Details For Training. \( \) 
In the I-frame training of the unified auto-encoder model, the network was optimized with a batch size of 8 and a patch size of \(256\times 256\)  randomly extracted from the training dataset. Two quality metrics (i.e., MSE and MS-SSIM) were used in the unified auto-encoder model.
In particular, Adam [1]} was used with multistage learning rates (\(\lbrace 1e-4, 5e-5, 1e-5, 5e-6, 1e-6\rbrace \) ) that changed with iteration boundaries (\(\lbrace 1600000, 2100000, 2300000, 2400000, 2500000\rbrace \) ). The value of Lagrange multiplier \(\lambda \)  was chosen from \(\lbrace 50, 105, 160, 300, 480, 710, 1000, 1780, 2915\rbrace \)  for MSE and \(\lbrace 3, 5, 8, 14, 20, 35, 52, 98, 145\rbrace \)  for MS-SSIM.
